Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition

no code implementations • 2 Feb 2023 • Peter Sunehag, Alexander Sasha Vezhnevets, Edgar Duéñez-Guzmán, Igor Mordach, Joel Z. Leibo

The algorithm we propose consists of two parts: an agent architecture and a learning rule.

Q-Learning reinforcement-learning +1

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

no code implementations • 14 Jul 2021 • Joel Z. Leibo, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charles Beattie, Igor Mordatch, Thore Graepel

Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess generalization to novel situations as their primary objective (unlike supervised-learning benchmarks).

Multi-agent Reinforcement Learning reinforcement-learning +1

Malthusian Reinforcement Learning

no code implementations • 17 Dec 2018 • Joel Z. Leibo, Julien Perolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel

Here we explore a new algorithmic framework for multi-agent reinforcement learning, called Malthusian reinforcement learning, which extends self-play to include fitness-linked population size dynamics that drive ongoing innovation.

Multi-agent Reinforcement Learning reinforcement-learning +1